• Home
  • Big Data Mining for Climate Change

Big Data Mining for Climate Change

Big Data Mining for Climate Change
  • Author : Zhihua Zhang
  • Publsiher : Anonim
  • Release : 01 December 2019
  • ISBN : 0128187034
  • Pages : 344 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKBig Data Mining for Climate Change

Summary:
Big Data Mining for Climate Change addresses how to manage the vast amount of information available for analysis. Climate change and its environmental, economic and social consequences are widely recognized as the biggest, most interconnected problem facing humanity. There is a huge amount of potential information currently available...and it is growing exponentially. This book walks through the latest research and how to navigate the resources available using big data applications. It is appropriate for scientists and advanced students studying climate change from a number of disciplines, including the atmospheric sciences, oceanic sciences, geography, environment sciences, ecology, energy, economics, engineering and public policy. Provides a step-by-step guide for applying big data mining tools to climate and environmental research Presents a comprehensive review of theory and algorithms of big data mining for climate change Includes current research in climate and environmental science as it relates to using big data algorithms


Big Data Mining for Climate Change

Big Data Mining for Climate Change
  • Author : Zhihua Zhang
  • Publisher : Anonim
  • Release : 01 December 2019
GET THIS BOOKBig Data Mining for Climate Change

Big Data Mining for Climate Change addresses how to manage the vast amount of information available for analysis. Climate change and its environmental, economic and social consequences are widely recognized as the biggest, most interconnected problem facing humanity. There is a huge amount of potential information currently available...and it is growing exponentially. This book walks through the latest research and how to navigate the resources available using big data applications. It is appropriate for scientists and advanced students studying


Big Data, Data Mining, and Machine Learning

Big Data, Data Mining, and Machine Learning
  • Author : Jared Dean
  • Publisher : John Wiley & Sons
  • Release : 07 May 2014
GET THIS BOOKBig Data, Data Mining, and Machine Learning

With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of


Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences
  • Author : Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
  • Publisher : CRC Press
  • Release : 01 August 2017
GET THIS BOOKLarge-Scale Machine Learning in the Earth Sciences

From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire


Data Mining and Knowledge Discovery for Big Data

Data Mining and Knowledge Discovery for Big Data
  • Author : Wesley W. Chu
  • Publisher : Springer Science & Business Media
  • Release : 24 September 2013
GET THIS BOOKData Mining and Knowledge Discovery for Big Data

The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the


Programming Collective Intelligence

Programming Collective Intelligence
  • Author : Toby Segaran
  • Publisher : "O'Reilly Media, Inc."
  • Release : 16 August 2007
GET THIS BOOKProgramming Collective Intelligence

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes


Reality Mining

Reality Mining
  • Author : Nathan Eagle,Kate Greene
  • Publisher : MIT Press
  • Release : 08 August 2014
GET THIS BOOKReality Mining

A look at how Big Data can be put to positive use, from helping users break bad habits to tracking the global spread of disease. Big Data is made up of lots of little data: numbers entered into cell phones, addresses entered into GPS devices, visits to websites, online purchases, ATM transactions, and any other activity that leaves a digital trail. Although the abuse of Big Data—surveillance, spying, hacking—has made headlines, it shouldn't overshadow the abundant positive applications


Data Mining and Knowledge Discovery for Big Data

Data Mining and Knowledge Discovery for Big Data
  • Author : Wesley W. Chu
  • Publisher : Springer Science & Business Media
  • Release : 24 September 2013
GET THIS BOOKData Mining and Knowledge Discovery for Big Data

The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the


Data Mining and Big Data

Data Mining and Big Data
  • Author : Ying Tan,Hideyuki Takagi,Yuhui Shi
  • Publisher : Springer
  • Release : 18 July 2017
GET THIS BOOKData Mining and Big Data

This book constitutes the refereed proceedings of the Second International Conference on Data Mining and Big Data, DMBD 2017, held in Fukuoka, Japan, in July/August 2017. The 53 papers presented in this volume were carefully reviewed and selected from 96 submissions. They were organized in topical sections named: association analysis; clustering; prediction; classification; schedule and sequence analysis; big data; data analysis; data mining; text mining; deep learning; high performance computing; knowledge base and its framework; and fuzzy control.


Data Mining and Learning Analytics

Data Mining and Learning Analytics
  • Author : Samira ElAtia,Donald Ipperciel,Osmar R. Zaïane
  • Publisher : John Wiley & Sons
  • Release : 20 September 2016
GET THIS BOOKData Mining and Learning Analytics

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of


Data Mining in Large Sets of Complex Data

Data Mining in Large Sets of Complex Data
  • Author : Robson Leonardo Ferreira Cordeiro,Christos Faloutsos,Caetano Traina Júnior
  • Publisher : Springer Science & Business Media
  • Release : 11 January 2013
GET THIS BOOKData Mining in Large Sets of Complex Data

The amount and the complexity of the data gathered by current enterprises are increasing at an exponential rate. Consequently, the analysis of Big Data is nowadays a central challenge in Computer Science, especially for complex data. For example, given a satellite image database containing tens of Terabytes, how can we find regions aiming at identifying native rainforests, deforestation or reforestation? Can it be made automatically? Based on the work discussed in this book, the answers to both questions are a


Mathematical and Physical Fundamentals of Climate Change

Mathematical and Physical Fundamentals of Climate Change
  • Author : Zhihua Zhang,John C. Moore
  • Publisher : Elsevier
  • Release : 06 December 2014
GET THIS BOOKMathematical and Physical Fundamentals of Climate Change

Mathematical and Physical Fundamentals of Climate Change is the first book to provide an overview of the math and physics necessary for scientists to understand and apply atmospheric and oceanic models to climate research. The book begins with basic mathematics then leads on to specific applications in atmospheric and ocean dynamics, such as fluid dynamics, atmospheric dynamics, oceanic dynamics, and glaciers and sea level rise. Mathematical and Physical Fundamentals of Climate Change provides a solid foundation in math and physics


Transparent Data Mining for Big and Small Data

Transparent Data Mining for Big and Small Data
  • Author : Tania Cerquitelli,Daniele Quercia,Frank Pasquale
  • Publisher : Springer
  • Release : 09 May 2017
GET THIS BOOKTransparent Data Mining for Big and Small Data

This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for


Investigating Social Problems

Investigating Social Problems
  • Author : A. Javier Trevino
  • Publisher : SAGE Publications
  • Release : 21 December 2017
GET THIS BOOKInvestigating Social Problems

The author is a proud sponsor of the 2020 SAGE Keith Roberts Teaching Innovations Award—enabling graduate students and early career faculty to attend the annual ASA pre-conference teaching and learning workshop. "Given the complexity of the issues, the study of social problems requires, indeed demands, specialized focus by experts." -A. Javier Treviño A. Javier Treviño, working with a panel of experts, thoroughly examines all aspects of social problems, providing a contemporary and authoritative introduction to the field. Each


Large Scale and Big Data

Large Scale and Big Data
  • Author : Sherif Sakr,Mohamed Gaber
  • Publisher : CRC Press
  • Release : 25 June 2014
GET THIS BOOKLarge Scale and Big Data

Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different


Machine Learning and Data Mining Approaches to Climate Science

Machine Learning and Data Mining Approaches to Climate Science
  • Author : Valliappa Lakshmanan,Eric Gilleland,Amy McGovern,Martin Tingley
  • Publisher : Springer
  • Release : 30 June 2015
GET THIS BOOKMachine Learning and Data Mining Approaches to Climate Science

This book presents innovative work in Climate Informatics, a new field that reflects the application of data mining methods to climate science, and shows where this new and fast growing field is headed. Given its interdisciplinary nature, Climate Informatics offers insights, tools and methods that are increasingly needed in order to understand the climate system, an aspect which in turn has become crucial because of the threat of climate change. There has been a veritable explosion in the amount of